Biology subjects

Christensen, M. H.

Publications and source records attributed to Christensen, M. H..

4 recordsLinked to original sources

bioRxiv2025

Unlocking Scalable Ligand Residence Time Predictions with Koffee Unbinding Kinetics Simulations

A great number of drug discovery programs fail due to poor in vivo efficacy and ADMET liabilities. On- and off-target ligand residence times can act as important drivers of these problems. While modern experimental techniques have made measuring compound kinetics data more routine, there is a lack of accurate, high-throughput simulation techniques to guide compound prioritization by residence time. In this work, we introduce Koffee Unbinding Kinetics as a solution to the hitherto unanswered problem of scalable ligand-protein residence time prediction. By bypassing conventional approaches based on molecular dynamics simulations, Koffee Unbinding Kinetics performs physics-based residence time screening at the atomistic level in {approx} 1 GPU minute per complex using inexpensive hardware, a speed-up of at least 3 - 5 orders of magnitude compared to current state-of-the-art simulation approaches. Koffee Unbinding Kinetics can enhance compound selection to mitigate costly future program failures by adding fast, predictive residence time simulations to early-stage computational drug discovery pipelines. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/686759v3_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@120d5dcorg.highwire.dtl.DTLVardef@b86b67org.highwire.dtl.DTLVardef@193554forg.highwire.dtl.DTLVardef@1c5c6c1_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2025

A novel reporter to visualize and quantify endogenous inflammasomes and caspase-1 recruitment

Inflammasomes are signaling complexes that coordinate inflammation by inducing rapid cytokine secretion and that protect against invading pathogens by initiating death of infected cells. Yet, current methods do not allow us to specifically monitor inflammasome activation in real-time at endogenous protein levels. To overcome this shortcoming, we developed and characterized a novel fluorescent reporter that visualizes inflammasome assembly and reports on the recruitment of the effector protein caspase-1 without impairing downstream signaling. The reporter permits the analysis of inflammasome assembly over time as well as precise quantification of cells with assembled inflammasomes. We have successfully applied the reporter in lentivirus-transduced human and murine cell lines and primary cells but have also applied recombinant viruses that encode the reporter in their genome to detect inflammasome responses in primary cells. Mouse intestinal enteroids expressing the reporter allowed us to visualize how Salmonella infection triggers NAIP/NLRC4 inflammasome assembly, cell death, and expulsion in complex tissues. Lastly, we apply the new tool to gain mechanistic insights and prove that caspase-1 activation on inflammasomes relies on the assembly of filaments of caspase-1CARD, which can be terminated by the CARD-only protein CARD17 to shut down cytokine secretion. We anticipate broad application of the reporter in fundamental and applied research, as it permits the quantitative assessment of inflammasome assembly at both high temporal and spatial resolution or in high throughput.

immunology↗

bioRxiv2022

DREAMS: Deep Read-level Error Model for Sequencing data applied to low-frequency variant calling and circulating tumor DNA detection

Circulating tumor DNA detection using Next-Generation Sequencing (NGS) data of plasma DNA is promising for cancer identification and characterization. However, the tumor signal in the blood is often low and difficult to distinguish from errors. We present DREAMS (Deep Read-level Modelling of Sequencing-errors) for estimating error rates of individual read positions. Using DREAMS, we developed statistical methods for variant calling (DREAMS-vc) and cancer detection (DREAMS-cc). For evaluation, we generated deep targeted NGS data of matching tumor and plasma DNA from 85 colorectal cancer patients. The DREAMS approach performed better than state-of-the-art methods for variant calling and cancer detection.

bioinformatics↗

bioRxiv2022

Pan-cancer association of DNA repair deficiencies with whole-genome mutational patterns

DNA repair deficiencies in cancers may result in characteristic mutational patterns, as exemplified by deficiency of BRCA1/2 and efficacy prediction for PARP-inhibitors. We trained and evaluated predictive models for loss-of-function (LOF) of 145 individual DDR genes based on genome-wide mutational patterns, including structural variants, indels, and base-substitution signatures. We identified 24 genes whose deficiency could be predicted with good accuracy, including expected mutational patterns for BRCA1/2, MSH3/6, TP53, and CDK12 LOF variants. CDK12 is associated with tandem-duplications, and we here demonstrate that this association can accurately predict gene deficiency in prostate cancers (area under the ROC curve=0.97). Our novel associations include mono- or biallelic LOF variants of ATRX, IDH1, HERC2, CDKN2A, PTEN, and SMARCA4, and our systematic approach yielded a catalogue of predictive models, which may provide targets for further research and development of treatment, and potentially help guide therapy.

genomics↗